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we demonstrate the use of the iornn by applying it to an ¡þ-order generative dependency model which is impractical for counting due to the problem of data sparsity---by applying the iornn to dependency parses , we have shown that using an ¡þ-order generative model for dependency | 1 |
word sense disambiguation is the process of determining which sense of a word is used in a given context---word sense disambiguation is the task of assigning sense labels to occurrences of an ambiguous word | 1 |
for nb and svm , we used their implementation available in scikit-learn---with two key properties : on-demand loading and a prefix tree structure for the source phrases | 0 |
we use the term-sentence matrix to train a simple generative topic model based on lda---dredze et al showed the possibility that many parsing errors in the domain adaptation tasks came from inconsistencies between annotation manners of training resources | 0 |
we implemented this model using the srilm toolkit with the modified kneser-ney discounting and interpolation options---with hyperedge replacement grammars , our implementations outperform the best previous system by several orders of magnitude | 0 |
semantic parsing is the task of automatically translating natural language text to formal meaning representations ( e.g. , statements in a formal logic )---semantic parsing is a domain-dependent process by nature , as its output is defined over a set of domain symbols | 1 |
for improving the word alignment , we use the word-classes that are trained from a monolingual corpus using the srilm toolkit---we trained the statistical phrase-based systems using the moses toolkit with mert tuning | 0 |
two popular evaluation metrics nist and bleu were chosen for automatic evaluation---for the automatic evaluation the two most popular and widely used metrics bleu and nist were used | 1 |
ushioda et al run a finite-state np parser on a pos-tagged corpus to calculate the relative frequency of the same six subcategorization verb classes---ushioda et al , 1993 , run a finite state np parser on a pos-tagged corpus to calculate the relative frequency of just six subcategorisation verb classes | 1 |
we present a joint model for the important qa tasks of answer sentence ranking and answer extraction---our joint model provides a precise mathematical formulation of answer chunk quality | 1 |
semantic parsing is the task of mapping natural language sentences to complete formal meaning representations---semantic parsing is the task of translating natural language utterances into a machine-interpretable meaning representation | 1 |
coreference resolution is the task of partitioning the set of mentions of discourse referents in a text into classes ( or ‘ chains ’ ) corresponding to those referents ( cite-p-12-3-14 )---coreference resolution is the process of finding discourse entities ( markables ) referring to the same real-world entity or concept | 1 |
using the exact match measure , the system performs with 76.0 % accuracy , and the baseline approach achieves 14.6 % accuracy---with the coder choice , the system performs with 76 . 0 % accuracy , and the baseline approach achieves only 14 . 6 % accuracy | 1 |
question answering ( qa ) is a well-studied problem in nlp which focuses on answering questions using some structured or unstructured sources of knowledge---question answering ( qa ) is a long-standing challenge in nlp , and the community has introduced several paradigms and datasets for the task over the past few years | 1 |
a 4-gram language model is trained on the xinhua portion of the gigaword corpus with the srilm toolkit---experimental results on duc2004 data sets and some expanded data demonstrate the good quality of our summaries | 0 |
on the 2nd of june , the team of japan will play world cup ( w cup ) qualification match against honduras in the second round of kirin cup at kobe wing stadium , the venue for the world cup---on the 2nd of june , the team of japan will play world cup ( w cup ) qualification match against honduras in the second round of kirin cup | 1 |
the reg module was trained in learning mode using the above reward function using the shar-sha reinforcement learning algorithm---the module was trained in learning mode using the sarsa reinforcement learning algorithm | 1 |
second , we devise an interactive upto-one alignment algorithm for assessing topic model stability---second , we devise an interactive alignment algorithm for matching latent topics | 1 |
we used the penn treebank wall street journal corpus---we used the penn wall street journal treebank | 1 |
the word embeddings are initialized using the pre-trained glove , and the embedding size is 300---the lstm system uses glove embeddings as its pretrained word vectors | 1 |
however , their evaluation has focused on particularly favorable conditions , limited to closely-related languages or comparable wikipedia corpora---however , their evaluation has focused on favorable conditions , using comparable corpora or closely-related languages | 1 |
we have encoded lexical semantic spaces of different languages by means of the same pivot language in order to make the languages comparable---smoothing is a standard technique to overcome this data sparsity problem | 0 |
specifically , we employ the seq2seq model with attention implemented in opennmt---we use opennmt 1 to train the nmt models discussed in this paper | 1 |
distributed word representations induced through deep neural networks have been shown to be useful in several natural language processing applications---in particular , the vector-space word representations learned by a neural network have been shown to successfully improve various nlp tasks | 1 |
we obtained both phrase structures and dependency relations for every sentence using the stanford parser---as a further test , we ran the stanford parser on the queries to generate syntactic parse trees | 1 |
research on automatic semantic structure extraction has been widely studied since the pioneering work of gildea and jurafsky---the advent of the supervised method proposed by gildea and jurafsky has led to the creation of annotated corpora for semantic role labeling | 1 |
we use srilm to train a 5-gram language model on the target side of our training corpus with modified kneser-ney discounting---in practice , the decoding for pos tagging over subwords is efficient | 0 |
to solve these problems , kiperwasser and goldberg propose a bi-lstm neural network parsing model---the use of a bi-lstm encoder in parsing was proposed independently by kiperwasser and goldberg and cross and huang | 1 |
in this work , we encode semantic features into convolutional layers by initializing them with important n-grams---based on mathematical methods , we encode semantic features into the filters | 1 |
word sense disambiguation ( wsd ) is the nlp task that consists in selecting the correct sense of a polysemous word in a given context---word sense disambiguation ( wsd ) is the task of automatically determining the correct sense for a target word given the context in which it occurs | 1 |
following ide and veronis we can distinguish between data-and knowledge-driven word sense disambiguation---following ide and veronis we distinguish between data-and knowledge-driven word sense disambiguation | 1 |
wordnet is a comprehensive lexical resource for word-sense disambiguation ( wsd ) , covering nouns , verbs , adjectives , adverbs , and many multi-word expressions---wordnet is a key lexical resource for natural language applications | 1 |
named entity recognition ( ner ) is the process by which named entities are identified and classified in an open-domain text---open ie in the monolingual setting has shown to be useful in a wide range of tasks , such as question answering , ontology learning , and summarization | 0 |
in our experiments , we use 300-dimension word vectors pre-trained by glove---in this paper , we present a novel approach which incorporates the web-derived selectional preferences | 0 |
we used a phrase-based smt model as implemented in the moses toolkit---we use the constrained decoding feature included in moses to this purpose | 1 |
as discussed at the end of section 2 , we have not included all the function tags or empty categories in our representation , a significant omission---as discussed at the end of section 2 , we have not included all the function tags or empty categories | 1 |
the language model was constructed using the srilm toolkit with interpolated kneser-ney discounting---we used a 5-gram language model with modified kneser-ney smoothing implemented using the srilm toolkit | 1 |
this supports the argument of jimeno et al that the use of disease terms in biomedical literature is well standardized---for example , jimeno et al argue that the use of disease terms in biomedical literature is well standardized , which is quite opposite for the gene terms | 1 |
the 5-gram language models were built using kenlm---a 5-gram language model built using kenlm was used for decoding | 1 |
miller and charles found evidence in several experiments that humans determine the semantic similarity of words from the similarity of the contexts they are used in---in one study , miller and charles found evidence that human subjects determine the semantic similarity of words from the similarity of the contexts they are used in | 1 |
the language model was constructed using the srilm toolkit with interpolated kneser-ney discounting---trigram language models were estimated using the sri language modeling toolkit with modified kneser-ney smoothing | 1 |
semantic role labeling ( srl ) is the task of identifying semantic arguments of predicates in text---semantic role labeling ( srl ) is a kind of shallow sentence-level semantic analysis and is becoming a hot task in natural language processing | 1 |
sentiwordnet describes itself as a lexical resource for opinion mining---sentiwordnet is another popular lexical resource for opinion mining | 1 |
we propose an adaptive ensemble method to adapt coreference resolution across domains---in this paper , we proposed an adaptive ensemble method for coreference resolution | 1 |
for the summarization task , we compare results using rouge---we measure the quality of the automatically created summaries using the rouge measure | 1 |
we used the stanford tagger to tag wsj and paraphrase datasets---in this paper , we propose to represent each word with an expressive multimodal distribution , for multiple distinct meanings | 0 |
previous work on identifying the semantic orientation of words has addressed the problem as both a semi-supervised and an unsupervised learning problem---previous work on identifying the semantic orientation of words has addressed the problem as both a semi-supervised and a weakly supervised learning problem | 1 |
the labeling cost can be significantly reduced by at least 80 % comparing with the supervised learning---pantel and lin , 2002 , introduce a method known as committee based clustering that discovers word senses | 0 |
we used a standard pbmt system built using moses toolkit---we use a pbsmt model built with the moses smt toolkit | 1 |
we use the stanford dependency parser to extract nouns and their grammatical roles---as for ej translation , we use the stanford parser to obtain english abstraction trees | 1 |
both files are concatenated and learned by word2vec---the word embeddings were obtained using word2vec 2 tool | 1 |
the significance tests were performed using the bootstrap resampling method---the grammar matrix is couched within the head-driven phrase structure grammar framework | 0 |
transliteration is often defined as phonetic translation ( cite-p-21-3-2 )---transliteration is a key building block for multilingual and cross-lingual nlp since it is useful for user-friendly input methods and applications like machine translation and cross-lingual information retrieval | 1 |
this paper reports on a series of experiments to explore the automatic acquisition of semantic classes for catalan adjectives---paper has presented an architecture for the semantic classification of catalan adjectives that explicitly includes polysemous classes | 1 |
we presented an api for recognition of extended named entities ( enes )---we present an extended named entity recognition api to recognize various types of entities | 1 |
we use 100-dimension glove vectors which are pre-trained on a large twitter corpus and fine-tuned during training---word sense disambiguation ( wsd ) is the task of determining the meaning of an ambiguous word in its context | 0 |
as this similarity , we employ distributional similarity , which is calculated using automatic dependency parses of 100 million japanese web pages---in this study , we apply a distributional similarity measure , which was computed from the web corpus used to construct the case frames | 1 |
in this work , we investigate the usefulness of spelling errors for the native language identification task---in this paper , we explore spelling errors as a source of information for detecting the native language | 1 |
somewhat surprisingly , we find that our approach is insensitive to the choice of pivot language---our approach is insensitive to the choice of pivot language , producing roughly the same alignments over six different pivot language choices | 1 |
we choose modified kneser ney as the smoothing algorithm when learning the ngram model---the language model is a large interpolated 5-gram lm with modified kneser-ney smoothing | 1 |
in this study , we analyzed the relationship between an individual¡¯s traits and his/her aspect framing decisions---in this study , we analyzed the relationship between an individual ¡¯ s traits and his / her aspect | 1 |
hochreiter and schmidhuber , 1997 ) proposed a long short-term memory network , which can be used for sequence processing tasks---long short-term memory was introduced by hochreiter and schmidhuber to overcome the issue of vanishing gradients in the vanilla recurrent neural networks | 1 |
parameters were tuned using minimum error rate training---the model weights are automatically tuned using minimum error rate training | 1 |
our model is a structured conditional random field---we use the mallet implementation of conditional random fields | 1 |
we first obtain word representations using the popular skip-gram model with negative sampling introduced by mikolov et al and implemented in the gensim package---word2vec and glove models are a popular choice for word embeddings , representing words by vectors for downstream natural language processing | 0 |
the language model was generated from the europarl corpus using the sri language modeling toolkit---the target-side language models were estimated using the srilm toolkit | 1 |
we study the impact of the degree of lexicalization and the training data size on the accuracy of dependency grammar induction---we conduct a systematic study regarding the impact of the degree of lexicalization and the training data size on the accuracy of grammar induction | 1 |
we used a list of positive and negative emoticons---we used two lists of positive and negative emoticons | 1 |
relation extraction is a fundamental task in information extraction---relation extraction is the task of detecting and classifying relationships between two entities from text | 1 |
the idea of extracting features for nlp using convolutional dnn was previously explored by collobert et al , in the context of pos tagging , chunking , named entity recognition and semantic role labeling---collobert et al used word embeddings as the input of various nlp tasks , including part-of-speech tagging , chunking , ner , and semantic role labeling | 1 |
the two baseline methods were implemented using scikit-learn in python---the models were implemented using scikit-learn module | 1 |
similar to zeng et al , we use pfs to specify entity pairs---similar to , we use position embeddings specified by entity pairs | 1 |
we used glove word embeddings with 300 dimensions pre-trained using commoncrawl to get a vector representation of the evidence sentence---we use the 300-dimensional pre-trained word2vec 3 word embeddings and compare the performance with that of glove 4 embeddings | 1 |
we used a caseless parsing model of the stanford parser for a dependency representation of the messages---we apply the rules to each sentence with its dependency tree structure acquired from the stanford parser | 1 |
we adopt the tool wapiti , which is an implementation of crf---we choose the crf learning toolkit wapiti 1 to train models | 1 |
finally , we combine all the above features using a support vector regression model which is implemented in scikit-learn---we use logistic regression with l2 regularization , implemented using the scikit-learn toolkit | 1 |
knowledge bases such as freebase and yago play a pivotal role in many nlp related applications---we present a hierarchical chunk-to-string translation model , which can be seen as a compromise between the hierarchical phrase-based model and the tree-to-string model | 0 |
coreference resolution is the task of clustering referring expressions in a text so that each resulting cluster represents an entity---coreference resolution is the task of determining whether two or more noun phrases refer to the same entity in a text | 1 |
specifically , factors such as frequency of contribution , proportion of turns , and number of successful interruptions have been identified as important indicators of influence---reid and ng identified that factors such as frequency of contribution , proportion of turns , and number of successful interruptions are important indicators of influence | 1 |
since this feature is affected by the source-side context , the decoder can choose a proper paraphrase and translate correctly---word sense disambiguation ( wsd ) is the nlp task that consists in selecting the correct sense of a polysemous word in a given context | 0 |
we computed pre-trained word embeddings in 300 dimensions for all the words in the stories using the skip-gram architecture algorithm---we used a freely-available pretrained model of 300 dimensions trained on approximately 100 billion words from news articles | 1 |
a combination of automated and human evaluations show that scpn s generate paraphrases that follow their target specifications without decreasing paraphrase quality when compared to baseline ( uncontrolled ) paraphrase systems---evaluations show that the generated paraphrases almost always follow their target specifications , while paraphrase quality does not significantly deteriorate compared to vanilla | 1 |
we compare our approach to cite-p-18-1-0 and senseclusters---dong et al represents questions using three cnns with different parameters when dealing with different answer aspects including answer path , answer context and answer type | 0 |
tang et al proposed td-lstm and tc-lstm , where target information is automatically taken into account---tang et al proposed target-dependent lstm to capture the aspect information when modeling sentences | 1 |
to train our models , we adopted svm-light-tk 7 , which enables the use of structural kernels in svm-light , with default parameters---to train our reranking models we used svm-light-tk 7 , which encodes structural kernels in svmlight solver | 1 |
we use glove vectors with 200 dimensions as pre-trained word embeddings , which are tuned during training---for the word-embedding based classifier , we use the glove pre-trained word embeddings | 1 |
lu et al consider the multilingual scenario where small amount of labeled data is available in the target language---meng et al and lu et al exploit parallel unlabeled data to bridge the language barrier | 1 |
in contrast , lexicalized reordering models are extensively used for phrase-based translation---a broadly used reordering model for phrase-based systems is lexicalized reordering | 1 |
a knsmoothed 5-gram language model is trained on the target side of the parallel data with srilm---the language model is trained on the target side of the parallel training corpus using srilm | 1 |
since coherence is a measure of how much sense the text makes , it is a semantic property of the text---twitter is the medium where people post real time messages to discuss on the different topics , and express their sentiments | 0 |
latent semantic analysis has been used to reduce the dimensionality of semantic spaces leading to improved performance---latent semantic analysis is also a widely used method for the automatic clustering of data along multiple dimensions | 1 |
this paper presents a comparative evaluation of several state-of-the-art english parsers based on different frameworks---in this paper , we present a comparative evaluation of syntactic parsers and their output | 1 |
the embedding layer was initialized using word2vec vectors---all word vectors are trained on the skipgram architecture | 1 |
the limited capacity of working memory is intrinsic to human sentence processing , and therefore must be addressed by any theory of human sentence processing---we use the svm implementation from scikit-learn , which in turn is based on libsvm | 0 |
in the second stage , a representative set of sentences are extracted and added to the summary in a reasonable order---in the second stage , a representative set of sentences are extracted and added to the summary | 1 |
the obtained triple translation model is also used for collocation translation extraction---for a global representation , features are often described using a continuous feature space , such as a color histogram in three different color spaces , or textures using gabor and haar wavelets | 0 |
coreference resolution is a multi-faceted task : humans resolve references by exploiting contextual and grammatical clues , as well as semantic information and world knowledge , so capturing each of these will be necessary for an automatic system to fully solve the problem---coreference resolution is the task of determining which mentions in a text are used to refer to the same real-world entity | 1 |
we use srilm for n-gram language model training and hmm decoding---for language models , we use the srilm linear interpolation feature | 1 |
using espac medlineplus , we trained an initial phrase-based moses system---we trained the statistical phrase-based systems using the moses toolkit with mert tuning | 1 |
but it has been shown to be effective for nlp and has achieved excellent results in sentence modeling , and other traditional nlp tasks---this has shown to be effective for numerous nlp tasks as it can capture word morphology and reduce out-of-vocabulary | 1 |
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